Hi scikit-learn community, I'm experimenting w/ unsupervised Deep Belief Nets (DBN) for dimension reduction.
Hinton shows good results using a 2000-500-250-125-2 Autoencoder to cluster a newswire corpus (essentially a neural topic model): http://www.cs.toronto.edu/%7Ehinton/science.pdf I'm trying to do something similar using a simple two step process: 1. Layered RBMs, trained w/ Contrastive Divergence (CD), and then 2. Gradient descent backpropagation on the DBN (weights established in step 1) I have a good handle on the first part (Layered RBM and CD). I'm using Edwin Chen's wonderfully documented and simple code -- https://github.com/echen/restricted-boltzmann-machines/blob/master/rbm.py Here's the related tutorial -- http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/ I'm not sure how to implement the second part -- running gradient descent backpropagation on the weights established by step 1. Has anyone tried this, or something similar? I found a library that uses MDP to do something similar -- http://organic.elis.ugent.be/node/270 -- but i'd like to do it all w/ Edwin's code + scikit-learn. Thanks, Timmy Wilson ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
